The classical shadow formalism and (some) implications for quantum machine learning
Sprache des Vortragstitels:
QSI Seminar, Centre for Quantum Software and Information, University of Technology of Sydney, Australia
Sprache des Tagungstitel:
Extracting important information from a quantum system as efficiently and tractably as possible is an important subroutine in most near-term applications of quantum hardware.
We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a classical shadow, can be used to predict many different properties. The required number of measurements is independent of the system size and saturates information-theoretic lower bounds.
If time permits, I will also illustrate how one can combine classical shadows with machine learning (ML). This combination showcases that training data obtained from quantum experiments can be very empowering for classical ML methods.